SoroushVahidi/scidocs huggingface dataset:
Improvement of Intrusion Detection System in Data Mining using Neural Network
(2013)

Many researchers have argued that Artificial Neural Networks (ANNs) can improve the performance of intrusion detection systems (IDS). One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper four different algorithms are used namely as Multilayer Perception, Radial Base Function, Logistic Regression and Voted Perception. All these neural based algorithms are implemented in WEKA data mining tool to evaluate the performance. For experimental work, NSL KDD dataset is used. Each neural based algorithm is tested with conducted dataset. It is shown that the Multilayer Perception neural network algorithm is providing more accurate results than other algorithms. Keywords— Data Mining, Intrusion Detection System, Neural network classifier algorithm, NSL KDD dataset
Valid items for screening dysphagia risk in patients with stroke: a systematic review.
(2012)

BACKGROUND AND PURPOSE
Screening for dysphagia is essential to the implementation of preventive therapies for patients with stroke. A systematic review was undertaken to determine the evidence-based validity of dysphagia screening items using instrumental evaluation as the reference standard.


METHODS
Four databases from 1985 through March 2011 were searched using the terms cerebrovascular disease, stroke deglutition disorders, and dysphagia. Eligibility criteria were: homogeneous stroke population, comparison to instrumental examination, clinical examination without equipment, outcome measures of dysphagia or aspiration, and validity of screening items reported or able to be calculated. Articles meeting inclusion criteria were evaluated for methodological rigor. Sensitivity, specificity, and predictive capabilities were calculated for each item.


RESULTS
Total source documents numbered 832; 86 were reviewed in full and 16 met inclusion criteria. Study quality was variable. Testing swallowing, generally with water, was the most commonly administered item across studies. Both swallowing and nonswallowing items were identified as predictive of aspiration. Neither swallowing protocols nor validity were consistent across studies.


CONCLUSIONS
Numerous behaviors were found to be associated with aspiration. The best combination of nonswallowing and swallowing items as well as the best swallowing protocol remains unclear. Findings of this review will assist in development of valid clinical screening instruments.
Distributed Subgradient Methods for Multi-Agent Optimization
(2009)

We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network over a time-varying topology. We provide convergence results and convergence rate estimates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.
Monte-Carlo Localization for Mobile Wireless Sensor Networks
(2006)

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo Localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors’ radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73%, on average 30%, for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55%, on average 22%. Finally, the processing time is reduced by 93% for a similar localization accuracy.
On the economic significance of ransomware campaigns: A Bitcoin transactions perspective
(2018)

Bitcoin cryptocurrency system enables users to transact securely and pseudo-anonymously by using an arbitrary number of aliases (Bitcoin addresses). Cybercriminals exploit these characteristics to commit immutable and presumably untraceable monetary fraud, especially via ransomware; a type of malware that encrypts files of the infected system and demands ransom for decryption. In this paper, we present our comprehensive study on all recent ransomware and report the economic impact of such ransomware from the Bitcoin payment perspective. We also present a lightweight framework to identify, collect, and analyze Bitcoin addresses managed by the same user or group of users (cybercriminals, in this case), which includes a novel approach for classifying a payment as ransom. To verify the correctness of our framework, we compared our findings on CryptoLocker ransomware with the results presented in the literature. Our results align with the results found in the previous works except for the final valuation in USD. The reason for this discrepancy is that we used the average Bitcoin price on the day of each ransom payment whereas the authors of the previous studies used the Bitcoin price on the day of their evaluation. Furthermore, for each investigated ransomware, we provide a holistic view of its genesis, development, the process of infection and execution, and characteristic of ransom demands. Finally, we also release our dataset that contains a detailed transaction history of all the Bitcoin addresses we identified for each ransomware.
Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization
(2018)

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector  $\ell _{p}$ -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the  $\ell _{p}$ -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.
Optimal Execution with Nonlinear Impact Functions and Trading-Enhanced Risk
(2001)

We determine optimal trading strategies for liquidation of a large single-asset portfolio to minimize a combination of volatility risk and market impact costs. We take the market impact cost per share to be a power law function of the trading rate, with an arbitrary positive exponent. This includes, for example, the square-root law that has been proposed based on market microstructure theory. In analogy to the linear model, we define a “characteristic time” for optimal trading, which now depends on the initial portfolio size and decreases as execution proceeds. We also consider a model in which uncertainty of the realized price is increased by demanding rapid execution; we show that optimal trajectories are described by a “critical portfolio size” above which this effect is dominant and below which it may be neglected.
Mining Offensive Language on Social Media
(2017)

English. The present research deals with the automatic annotation and classification of vulgar ad offensive speech on social media. In this paper we will test the effectiveness of the computational treatment of the taboo contents shared on the web, the output is a corpus of 31,749 Facebook comments which has been automatically annotated through a lexicon-based method for the automatic identification and classification of taboo expressions. Italiano. La presente ricerca affronta il tema dell’annotazione e della classificazione automatica dei contenuti volgari e offensivi espressi nei social media. Lo scopo del nostro lavoro consiste nel testare l’efficacia del trattamento computazionale dei contenuti tabù condivisi in rete. L’output che forniamo un corpus di 31,749 commenti generati dagli utenti di Facebook e annotato automaticamente attraverso un metodo basato sul lessico per l’identificazione e la classificazione delle
Incremental Algorithms for Hierarchical Classification
(2004)

We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchical loss function, the H-loss, implementing the simple intuition that additional mistakes in the subtree of a mistaken class should not be charged for. Based on a probabilistic data model introduced in earlier work, we derive the Bayes-optimal classifier for the H-loss. We then empirically compare two incremental approximations of the Bayes-optimal classifier with a flat SVM classifier and with classifiers obtained by using hierarchical versions of the Perceptron and SVM algorithms. The experiments show that our simplest incremental approximation of the Bayes-optimal classifier performs, after just one training epoch, nearly as well as the hierarchical SVM classifier (which performs best). For the same incremental algorithm we also derive an H-loss bound showing, when data are generated by our probabilistic data model, exponentially fast convergence to the H-loss of the hierarchical classifier based on the true model parameters.
An integrated framework for optimizing automatic monitoring systems in large IT infrastructures
(2013)

The competitive business climate and the complexity of IT environments dictate efficient and cost-effective service delivery and support of IT services. These are largely achieved by automating routine maintenance procedures, including problem detection, determination and resolution. System monitoring provides an effective and reliable means for problem detection. Coupled with automated ticket creation, it ensures that a degradation of the vital signs, defined by acceptable thresholds or monitoring conditions, is flagged as a problem candidate and sent to supporting personnel as an incident ticket. This paper describes an integrated framework for minimizing false positive tickets and maximizing the monitoring coverage for system faults.
 In particular, the integrated framework defines monitoring conditions and the optimal corresponding delay times based on an off-line analysis of historical alerts and incident tickets. Potential monitoring conditions are built on a set of predictive rules which are automatically generated by a rule-based learning algorithm with coverage, confidence and rule complexity criteria. These conditions and delay times are propagated as configurations into run-time monitoring systems. Moreover, a part of misconfigured monitoring conditions can be corrected according to false negative tickets that are discovered by another text classification algorithm in this framework. This paper also provides implementation details of a program product that uses this framework and shows some illustrative examples of successful results.
Fast Exact Inference with a Factored Model for Natural Language Parsing
(2002)

We presenta novel generati ve model for natural languagetree structuresin whichsemantic(lexical dependenc y) andsyntacticstructuresare scoredwith separatemodels.Thisfactorizationprovidesconceptual simplicity, straightforwardopportunitiesfor separatelyimproving the componentmodels,anda level of performancealreadycloseto thatof similar, non-factoredmodels.Most importantly, unlikeothermodernparsing models,thefactoredmodeladmitsanextremelyeffectiveA parsingalgorithm,which makesefficient,exactinferencefeasible.
Large margin hierarchical classification
(2004)

We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded by a rooted tree which induces a metric over the label set. Our approach combines ideas from large margin kernel methods and Bayesian analysis. Following the large margin principle, we associate a prototype with each label in the tree and formulate the learning task as an optimization problem with varying margin constraints. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent labels in the hierarchy. We describe new online and batch algorithms for solving the constrained optimization problem. We derive a worst case loss-bound for the online algorithm and provide generalization analysis for its batch counterpart. We demonstrate the merits of our approach with a series of experiments on synthetic, text and speech data.
A ZigBee -wireless wearable remote physiological monitoring system
(2016)

Wearable health monitoring systems use integrated sensors to monitor vital physiological parameters of the wearer. The paper discusses the preliminary results of a prototype wearable physiological monitoring system to monitor physiological parameters such as Electrocardiogram (ECG), Heart Rate (HR), Electroencephalogram (EEG) and Body Temperature. The ECG and EEG signals are acquired using a textile electrodes integrated into the fabric of the wearer. The acquired ECG and EEG signals are processed to remove noises and the parameters are extracted and trend analysis is carried out. The physiological parameters are monitored at the remote monitoring station using a ZigBee wireless communication.
A survey of datasets for visual tracking
(2015)

For 15 years now, visual tracking has been a very active research area of the computer vision community. But an increasing amount of works can be observed in the last five years. This has led to the development of numerous algorithms that can deal with more and more complex video sequences. Each of them has its own strengths and weaknesses. That is the reason why it becomes necessary to compare those algorithms. For this purpose, some datasets dedicated to visual tracking as well as, sometimes, their ground truth annotation files are regularly made publicly available by researchers. However, each dataset has its own specificities and is sometimes dedicated to test the ability of some algorithms to tackle only one or a few specific visual tracking subproblems. This article provides an overview of some of the datasets that are most used by the visual tracking community, but also of others that address specific tasks. We also propose a cartography of these datasets from a novel perspective, namely that of the difficulties the datasets present for visual tracking.
DC Voltage Balancing for PWM Cascaded H-Bridge Converter Based STATCOM
(2006)

This paper presents a new control method for cascaded connected H-bridge converter based STATCOMs. These converters have been classically commutated at fundamental line-frequencies, but the evolution of power semiconductors has allowed to increase switching frequencies and the power ratings of these devices, permitting the use of PWM modulation techniques. This paper mainly focuses the DC bus voltage balancing problems, and proposes a new control technique that solves these balancing problems maintaining the delivered reactive power equally distributed among all the H-bridges of the converter
Emotion recognition from text using semantic labels and separable mixture models
(2006)

This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.
Project Aura: Toward Distraction-Free Pervasive Computing
(2002)

A s the effects of Moore's law cause computing systems to become cheaper and more plentiful, a new problem arises: increasingly, the bottleneck in computing is not its disk capacity, processor speed, or communication bandwidth, but rather the limited resource of human attention. Human attention refers to a user's ability to attend to his or her primary tasks, ignoring system-generated distractions such as poor performance and failures. By exploiting plentiful computing resources to reduce user distraction, Project Aura is creating a system whose effectiveness is considerably greater than that of other systems today. Aura is specifically intended for pervasive computing environments involving wireless communication , wearable or handheld computers, and smart spaces. Human attention is an especially scarce resource in such environments, because the user is often preoccupied with walking, driving, or other real-world interactions. In addition, mobile computing poses difficult challenges such as intermittent and variable-bandwidth connectivity, concern for battery life, and the client resource constraints that weight and size considerations impose. To accomplish its ambitious goals, research in Aura spans every system level: from the hardware, through the operating system, to applications and end users. Underlying this diversity of concerns, Aura applies two broad concepts. First, it uses proactivity, which is a system layer's ability to anticipate requests from a higher layer. In today's systems , each layer merely reacts to the layer above it. Second, Aura is self-tuning: layers adapt by observing the demands made on them and adjusting their performance and resource usage characteristics accordingly. Currently, system-layer behavior is relatively static. Both of these techniques will help lower demand for human attention. To illustrate the kind of world we are trying to create, we present two hypothetical Aura scenarios. Although these might seem far-fetched today, they represent the kind of scenarios we expect to make commonplace through our research. In the first scenario, Jane is at Gate 23 in the Pitts-burgh airport, waiting for her connecting flight. She has edited many large documents and would like to use her wireless connection to email them. Unfortunately , bandwidth is miserable because many passengers at Gates 22 and 23 are surfing the Web. Aura observes that, at the current bandwidth, Jane won't be able to finish sending her documents before her flight departs. Consulting the airport's wireless network bandwidth service and flight schedule service, Aura discovers that wireless bandwidth is excellent at Gate 15, and that there are …
Potassium and sodium transporters of Pseudomonas aeruginosa regulate virulence to barley
(2008)

We investigated the role of three uncharacterized cation transporters of Pseudomonas aeruginosa PAO1 as virulence factors for barley: PA1207, PA5021, and PA2647. PAO1 displayed reduced barley virulence with inactivated PA1207, PA5021, and PA2647 as well as with one known Na+/H+ antiporter, PA1820. Using the Escherichia coli LB2003 mutant lacking three K+ uptake systems, the expression of the PA5021 gene repressed LB2003 growth with low K+, but the strain acquired tolerance to high K+. In contrast, the expression of the PA1207 gene enhanced growth of LB2003 with low K+ but repressed its growth with high K+; therefore, the PA5021 protein exports K+, while the PA1207 protein imports K+. The PA5021 mutant of P. aeruginosa also showed impaired growth at 400 mM KCl and at 400 mM NaCl; therefore, the PA5021 protein may also export Na+. The loss of the PA5021 protein also decreased production of the virulence factor pyocyanin; corroborating this result, pyocyanin production decreased in wild-type PAO1 under high salinity. Whole-genome transcriptome analysis showed that PAO1 induced more genes in barley upon infection compared to the PA5021 mutant. Additionally, PAO1 infection induced water stress-related genes in barley, which suggests that barley may undergo water deficit upon infection by this pathogen.